The performance of the proposed solutions have been evaluated numerically by using the PyTorch DL framework. Scenario “O1-28 GHz” of the deepMIMO channel model [1]} is employed to generate the dataset. The BS is equipped with \(N_{\sf {T}} = 64\)  antennas and \(N_{\sf {RF}} = 8\)  RF chains with \(q\) -bit PS serving \(N_{\sf {U}}=4\)  users located randomly in a dedicated area (in deepMIMO channel model \(\texttt {active\_user\_first} = 1100\)  and \(\texttt {active\_user\_last} = 2200\) ).
The size of the DNN dataset is set to \(2 \times 10^{6}\)  samples, with 85% of the samples used for the training set and the remaining ones used to evaluate the performance. The mini-batch size, learning rate, and weight decay are set to 1000, \(0.001\) , and \(10^{-5}\) , respectively. We used “RAdam” as the DNN training optimizer [2]}. The training procedure converged after approximately 200 epochs.
